2009/01/01 by Wai‐Tat Fu, Thomas Kannampallil, Ruogu Kang · 44 citations
Computer Science · Physics and Astronomy · #Topic Modeling #Opinion Dynamics and Social Influence #Complex Network Analysis Techniques #Computer science #Inference #Semantics (computer science) #Natural language processing #Artificial intelligence #Imitation #Rank (graph theory) #Topic model #Word (group theory) #Process (computing) #Interpretation (philosophy) #Probabilistic logic #Domain (mathematical analysis) #Linguistics
paper · doi:10.1109/cse.2009.382
openalex publication_date 2009/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We describe a semantic imitation model of social tagging that integrates formal representations of semantics and a stochastic tag choice process to explain and predict emergent behavioral patterns. The model adopts a probabilistic topic model to separately represent external word-topic and internal word-concept relations. These representations are coupled with a tag-based topic inference process that predicts how existing tags may influence the semantic interpretation of a document. The inferred topics influence the choice of tags assigned to a document through a random utility model of tag choices. We show that the model is successful in explaining the stability in tag proportions across time and power-law frequency-rank distributions of tag co-occurrences for semantically general and narrow tags. The model also generates novel predictions on how emergent behavioral patterns may change when users with different domain expertise interact with a social tagging system. The model demonstrates the weaknesses of single-level analyses and highlights the importance of adopting a multi-level modeling approach to explain online social behavior.